Abstract
It is believed that the Tibetan Plateau plays an important role in shaping the global atmospheric circulation and climate change. Due to the lack of observations in its surrounding area, previous studies mostly relied on numerical and empirical models to characterize the atmosphere over the Tibetan Plateau. For example, NRLMSISE-00 is a neutral atmospheric empirical model that has been widely used. In this study, NRLMSISE-00 model temperature data were compared with radiosonde temperature profiles obtained over Golmud, which is located in the northeast part of the Tibetan Plateau. Certain degree of deviations has been observed, especially in the warm season (here we defined from May 16 to October 15), with the temperature difference up to 19 K. A three-layer feed-forward neural network (NN) has been built to improve the NRLMSISE-00 model. Radiosonde temperature profiles from year 2013 to 2014 were used as training database for this NN. The radiosonde temperatures in year 2015 were then used to evaluate this method. Our results showed that the deviations between the NRLMSISE-00 model and the in situ radiosonde data have been significantly reduced. This study demonstrates the feasibility of NN for improving the atmospheric model accuracy using radiosonde data as training samples.
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